Message Digest 5 (MD-5) Decryption Application using Python-Based Dictionary Attack Technique

Neforawati, Indri; Arnaldy, Defiana

doi:10.1109/ic2ie53219.2021.9649020

Cited by 3 publications

(5 citation statements)

References 18 publications

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“…almost all operating systems are supported by the Python language, even for the Linux operating system, almost all distributions include Python in it. With a code that is simple and easy to implement, a programmer can prioritize the development of the application he is making, instead of being busy looking for syntax errors [19] [20].…”

Section: Methodsmentioning

confidence: 99%

Mask detection system at the entry of a room

Herdiyanto,

Fadlil

2023

Sig.Img.Proc.Lett

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This study focuses on automatic mask detection tools that can open doors in a room to minimize violations of health protocols, one of which is the use of masks during the pandemic. The method used in this study is the CNN classification method. Where the CNN calcification method has several stages in it, including pre-processing, training, and testing. In the pre-processing, all image data used will be labeled using Labeling.axe. The training process at CNN uses TensorFlow framework version 1.15. In the testing process, the test and data testing will be carried out in real-time by entering new images and models that are made and then a classification process is carried out on objects caught by the camera, classified images are marked with boxes and names of data classes. This data class is divided into two, namely data on wearing masks and without masks. The results of the test were carried out by entering 200 facial image data. The system can correctly detect as much as 190 times from 200 data tested with an Accuracy rate of 95%. Based on the test results, it shows that the resulting model is good and suitable for the classification process of recognizing mask detection images. However, to produce a better model requires data with more variety and a larger amount of data.

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Section: Methodsmentioning

confidence: 99%

Mask detection system at the entry of a room

Herdiyanto,

Fadlil

2023

Sig.Img.Proc.Lett

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“…The STG Simulation APP structure is shown in Figure 4 The data model is divided into a user information model and a simulation information model. The user information model is used to store basic user information such as user name, password, user role and the permission to use the simulation model, where the password is stored with salted MD5 encryption to prevent dictionary attacks [8]. The simulation information model stores basic parameters such as title, description, simulation model, simulation input and output parameters and supercomputing platform operation parameters.…”

Section: The Stg Simulation App Buildmentioning

confidence: 99%

Design of steam turbine generator simulation APP based on industrial internet platform

Lin,

Duan,

Ding

2023

Fourth International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2023)

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In the R&D of high-end equipment, simulation software is often used to assist design. However, the problems such as high cost, difficulty and expensive supporting hardware of traditional simulation software limit the application range of simulation technology. In order to solve this problem, based on HPCClouds Industrial Internet Platform and supercomputing technology, a steam turbine generator Simulation APP was built according to the specific simulation requirements of large steam turbine generator manufacturing industry, and its usability and advantages were tested. The results showed that the use cost of Simulation APP was reduced by the simulation software and supercomputing technology in HPCClouds Industrial Internet Platform, and the use range of the platform was expanded by the subscription system, so as to realize the universality of simulation technology.

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“…These studies obtained different objectives such as response time, tardiness, and network delay. An integrated particle swarm optimization-enabled scheme was presented by [ 3 , 4 , 15 , 17 ], where a message digest (MD5) scheme was integrated with a particle swarm optimization (PSO) algorithm to secure healthcare tasks in the IoT network. The goal was to minimize security risks in the network, and these studies considered fine-grained tasks and homogeneous networks in their models.…”

Section: Related Workmentioning

confidence: 99%

“…The main goal was to reduce local and global search times for scheduling on heterogeneous fog and cloud nodes in the system, establish a secure environment among connected nodes, and minimize attack risk in the IoT network. These dynamic approaches can identify anomalies and resource performance of applications, but there was still uncertainly of heterogeneous nodes in terms of scalability [ 17 , 18 , 19 , 20 ].…”

Section: Related Workmentioning

confidence: 99%

“…In this section, existing performance workflow schemes [ 15 , 17 , 19 , 20 ] are compared with proposed scheme LSEOS on the basis of the performance of workflow applications. The IFogsim framework was used, where proposed and existing approaches were efficiently implemented and their performance in the architecture was evaluated.…”

Section: Performance Evaluationmentioning

confidence: 99%

See 1 more Smart Citation

A Lightweight Secure Adaptive Approach for Internet-of-Medical-Things Healthcare Applications in Edge-Cloud-Based Networks

Lakhan

Sodhro

Majumdar

et al. 2022

Sensors

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Mobile-cloud-based healthcare applications are increasingly growing in practice. For instance, healthcare, transport, and shopping applications are designed on the basis of the mobile cloud. For executing mobile-cloud applications, offloading and scheduling are fundamental mechanisms. However, mobile healthcare workflow applications with these methods are widely ignored, demanding applications in various aspects for healthcare monitoring, live healthcare service, and biomedical firms. However, these offloading and scheduling schemes do not consider the workflow applications’ execution in their models. This paper develops a lightweight secure efficient offloading scheduling (LSEOS) metaheuristic model. LSEOS consists of light weight, and secure offloading and scheduling methods whose execution offloading delay is less than that of existing methods. The objective of LSEOS is to run workflow applications on other nodes and minimize the delay and security risk in the system. The metaheuristic LSEOS consists of the following components: adaptive deadlines, sorting, and scheduling with neighborhood search schemes. Compared to current strategies for delay and security validation in a model, computational results revealed that the LSEOS outperformed all available offloading and scheduling methods for process applications by 10% security ratio and by 29% regarding delays.

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Message Digest 5 (MD-5) Decryption Application using Python-Based Dictionary Attack Technique

Cited by 3 publications

References 18 publications

Mask detection system at the entry of a room

Mask detection system at the entry of a room

Design of steam turbine generator simulation APP based on industrial internet platform

A Lightweight Secure Adaptive Approach for Internet-of-Medical-Things Healthcare Applications in Edge-Cloud-Based Networks

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